DNA amplification methods provide a powerful and widely used tool for genomic analysis. Polymerase chain reaction (PCR) methods, for example, permit quantitative analysis to determine DNA copy number, sample source quantitation, and transcription analysis of gene expression. DNA analysis methods allow the detection of single base changes in specific regions of the genome, such as single nucleotide polymorphisms (SNPs), SNP analysis and other techniques facilitate the identification of mutations associated with specific diseases and conditions, such as various cancers, thalassemia, or others.
Many applications of PCR require the accurate generation of desired amplification products versus the production of undesired artifacts. One useful approach for validating the integrity of PCR reactions relies on melting curve analysis to discriminate artifact from real amplification product. Melting curve analysis can also be used to differentiate the various products of multiplexed DNA amplification, and to extend the dynamic range of quantitative PCR. DNA melting curve analysis can also be a powerful tool for optimizing PCR thermal cycling conditions, since the point at which DNA fragments or other material melts and separate can be more accurately pinpointed.
One known approach for DNA melting curve analysis utilizes fluorescence monitoring with intercalating double-strand-DNA specific dyes, such as for example, SYBR Green. The SYBR Green dye attaches to the DNA as double-stranded DNA amplification products are formed, and continues to bind to the DNA as long as the DNA remains double-stranded. When melting temperatures are reached, the denaturation or melting of the double-stranded DNA is indicated and can be observed by a significant reduction in fluorescence, as SYBR Green dissociates from the melted strand. The detected dye fluorescence intensity typically decreases about 1000-fold during the melting process. Plotting fluorescence as a function of temperature as the sample heats through the dissociation temperature produces a DNA melting curve. The shape and position of the DNA melting curve is a function of the DNA sequence, length, and GC/AT content.
Currently known dissociation/melting curve analysis methods calculate and display the first derivative of multi-component dye intensity data versus temperature, i.e., the differential melting curve. The temperature, Tm, at a peak of the differential melting curve characterizes the product of the biochemical reaction. A sample with multiple amplification products will show a melt curve with multiple peaks in the differential melt curve. See generally, for example, FIG. 1 (illustrating a single sample) and FIGS. 2(A) and 2(B) (illustrating multiple samples).
Typically, during melting curve analysis, the raw data fluorescence measurements are taken at uneven or irregular temperature intervals. This can introduce undesired sensitivity to the sampling process along the temperature axis. Conventional signal processing techniques such as filtering, differentiation, and the like, do not apply for data samples at uneven temperature intervals. There is a need for techniques that correct for uneven or irregular temperature interval sampling, and other problems in the field.
For example, calculating the differential dissociation curve can be a noisy process. The melt curve is inherently noisy, due, for example, to sampling or quantization errors, and traditional computational differentiation methods can make noise issues worse. There is a need for techniques that distinguish a genuine signal peak versus a noisy spike, and for techniques that distinguish a sample producing credible melting curve results, versus a sample producing unintelligible data.
Current dissociation curve analysis methods, moreover, typically assume a single peak in a differential dissociation curve. There is a need for melting curve analysis methods for gene expression and other purposes that can detect multiple peaks of a differential melting curve. There is a further need for melting curve techniques that can be applied to, or implemented in, automated validation techniques, among other applications.